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      <title>常见AI大模型评估 - 交互式测验</title>
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    </head>
    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>常见AI大模型评估 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
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                    <div id="question-container">
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                  <button id="next-btn" class="nav-btn">下一题</button>
                </div>
            </div>
          </div>
      </div>

      <script>
        const quizData = [
  {
    "question": "根据文档，Anthropic公司的Claude 3系列模型在哪类应用场景中表现尤为突出，其核心优势是什么？",
    "options": [
      "与Google生态系统（如Android开发）深度集成，并拥有原生搜索能力。",
      "代码审查、重构和复杂文档分析，因为它以长上下文支持和低幻觉率著称。",
      "作为完全开源的模型，特别适合需要本地部署和对数据安全有严格要求的项目。",
      "拥有顶尖的多模态处理能力和极强的通用推理能力，但API成本较高。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档明确指出，Claude 3系列以其长上下文支持和低幻觉率而闻名，这使得它在处理需要深入理解和分析大量文本或代码的场景（如代码审查、重构、复杂文档分析）中表现出色。",
    "source": "文档中「2.2 Claude 3系列 (Anthropic)」部分",
    "hint": "文档中提到该模型“以长上下文支持和低幻觉率著称”，并列举了其具体的应用场景。"
  },
  {
    "question": "在讨论企业级AI工具部署时，文档提到了“本地部署”方式。选择这种方式最主要的原因是什么？",
    "options": [
      "架构最为简单，集成挑战小，几乎没有维护成本。",
      "可以享受持续的模型更新，并且无需投入任何硬件成本。",
      "数据安全可控、无网络延迟，并且长期来看成本可能更低。",
      "灵活性最高，能自动平衡敏感数据和非敏感数据的处理，是所有企业的首选。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档中的对比表格明确列出了本地部署的核心优势，包括：数据安全可控、无网络延迟和长期成本可控。这对于金融、医疗等对数据隐私有严格要求的行业尤为重要。而快速部署和无硬件维护是云服务的优势。",
    "source": "文档中「5.2 企业级部署考量」部分",
    "hint": "请查看文档中关于不同部署方式优劣势的对比表格，关注“本地部署”的“优势”一栏。"
  },
  {
    "question": "文档在“DevOps与CI/CD中的AI应用”部分提到了GitLab的一项智能功能，这项功能的主要目的是什么？",
    "options": [
      "自动修复在CI/CD流水线中检测到的代码缺陷。",
      "通过AI预测并优先运行最有可能失败的测试用例，以缩短开发周期。",
      "自动为项目生成100%覆盖率的单元测试用例。",
      "在代码提交时，自动生成符合规范的commit message。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档中的YAML示例展示了`pytest --smart-selection`命令，并解释其作用是“AI选择最可能失败的测试优先运行”。这种策略旨在更快地发现潜在问题，从而使整个CI/CD流程更高效。",
    "source": "文档中「1.4 DevOps与CI/CD中的AI应用」部分",
    "hint": "注意文档中的代码示例 `.gitlab-ci.yml` 以及旁边的注释说明。"
  },
  {
    "question": "相较于GPT、Claude等国际主流模型，文档认为国内大模型（如文心一言、通义千问）的核心优势体现在哪个方面？",
    "options": [
      "它们都已完全开源，并提供比Llama系列更多的模型规格。",
      "在语音交互和多模态能力上已全面超越所有国际模型。",
      "在处理中文语境的内容、编程注释和技术文档时更为精准。",
      "它们是唯一能够与国内云服务深度集成的AI模型。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档明确指出：“国内模型在处理中文内容方面具有明显优势”，并展示了DeepSeek处理中文注释的示例。这表明它们对中文的理解和处理能力是其关键的差异化优势。",
    "source": "文档中「2.5 国内大模型」部分",
    "hint": "文档开头第一句就点明了这类模型的“明显优势”是什么。"
  },
  {
    "question": "在“数据库与系统架构”的应用场景中，文档展示了AI模型如何为开发者提供帮助？",
    "options": [
      "自动将整个数据库从一种类型（如MySQL）迁移到另一种类型（如PostgreSQL）。",
      "根据需求描述，自动生成完整的数据库表结构和ER图。",
      "分析现有的SQL查询，并提出具体的性能优化建议，例如添加索引。",
      "直接连接到生产数据库并执行数据清理和备份任务。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档中的示例清晰地展示了一个原始的、性能可能不佳的SQL查询，以及Claude模型给出的“性能问题分析”和“优化建议”，其中就包括了具体的“索引创建建议”。这体现了AI在代码优化方面的应用价值。",
    "source": "文档中「4.3 数据库与系统架构」部分",
    "hint": "回顾文档中关于SQL查询的示例，看看AI模型提供了怎样的“优化建议”。"
  },
  {
    "question": "根据文档，像CodeLlama、StarCoder2和Mistral系列这类开源代码特化模型，最适合哪一类应用场景？",
    "options": [
      "需要与特定云厂商（如Google Cloud）生态系统深度集成的项目。",
      "需要最前沿的多模态处理能力（如视频、音频分析）的大型商业应用。",
      "资源受限或需要本地化部署的环境，例如私有开发或边缘计算。",
      "仅用于自然语言聊天，而不涉及任何代码生成或解释的场景。"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档在介绍这些模型时明确总结道：“这些模型特别适合资源受限或需要本地部署的环境”。开源和轻量化的特性使得它们在数据隐私、低延迟和成本控制方面具有优势。",
    "source": "文档中「2.6 开源代码特化模型」部分",
    "hint": "在介绍完WizardCoder的代码解释示例后，文档对这类模型的适用环境做出了明确的总结。"
  },
  {
    "question": "文档中Google的Gemini模型被强调的一个独特优势是什么，使其在特定开发项目中表现突出？",
    "options": [
      "它是市面上唯一一个提供从Nano到Advanced全规格的模型。",
      "与Google自身的技术生态系统（如Android开发、Firebase、Google Maps API）能够深度集成。",
      "以其极低的幻觉率和业界最长的上下文窗口处理复杂文档而闻名。",
      "它是完全开源的，允许企业进行无限制的本地部署和微调。"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档在介绍Gemini时，特别提到了其“与Google生态系统深度集成”的特性，并用一个生成Android代码（使用Google Maps API）的例子来佐证这一点。这使得它在开发与Google服务相关的应用时尤其得心应手。",
    "source": "文档中「2.3 Gemini (Google)」部分",
    "hint": "思考一下，作为一个由Google开发模型，它在处理自家技术栈时会有什么天然的优势？文档中的代码示例也揭示了这一点。"
  }
];
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            const totalQuestions = quizData.length;
            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
            const accuracy = totalQuestions > 0 ? Math.round((correctCount / totalQuestions) * 100) : 0;
            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

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                '<h2>答题报告</h2>' +
                '<div class="score-summary">' +
                '<div class="score-card">' +
                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
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                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
                '<button id="review-btn">回顾测验</button>' +
                '<button class="primary" onclick="location.reload()">再试一次</button>' +
                '</div>' +
                '</div>';

            let reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  